02348nas a2200193   4500008004100000022001300041245009300054210006900147260000800216300001200224490000600236520170200242100001601944700002001960700001501980700001801995700001602013856012502029       2018                        eng d  a2095829300aPrediction of coal ash fusion temperatures using computational intelligence based models0 aPrediction of coal ash fusion temperatures using computational i  cDEC  a486-5070 v43 a<p>In the coal-based combustion and gasification processes, the mineral matter contained in the coal (predominantly oxides), is left as an incombustible residue, termed ash. Commonly, ash deposits are formed on the heat absorbing surfaces of the exposed equipment of the combustion/gasification processes. These deposits lead to the occurrence of slagging or fouling and, consequently, reduced process efficiency. The ash fusion temperatures (AFTs) signify the temperature range over which the ash deposits are formed on the heat absorbing surfaces of the process equipment. Thus, for designing and operating the coal-based processes, it is important to have mathematical models predicting accurately the four types of AFTs namely initial deformation temperature, softening temperature, hemispherical temperature, and flow temperature. Several linear/nonlinear models with varying prediction accuracies and complexities are available for the AFT prediction. Their principal drawback is their applicability to the coals originating from a limited number of geographical regions. Accordingly, this study presents computational intelligence (CI) based nonlinear models to predict the four AFTs using the oxide composition of the coal ash as the model input. The CI methods used in the modeling are genetic programming (GP), artificial neural networks, and support vector regression. The notable features of this study are that the models with a better AFT prediction and generalization performance, a wider application potential, and reduced complexity, have been developed. Among the CI-based models, GP and MLP based models have yielded overall improved performance in predicting all four AFTs.</p>1 aTambe, S.S.1 aNaniwadekar, M.1 aTiwary, S.1 aMukherjee, A.1 aDas, T., B.  uhttps://library.ncl.res.in/content/prediction-coal-ash-fusion-temperatures-using-computational-intelligence-based-models